Sustainable Kapok/cotton/graphene-based textiles for thermal regulation and moisture control with innovative composite
Bibliographic record
Abstract
Personal thermal and moisture management (PTAMM) clothing is essential to maintain human comfort and safeguard sports health when doing outdoor sports and adventures in cold environment. In this work, different kinds of composite yarns were spun from graphene polyester filament, kapok and cotton fibers (KCG yarns) using ELS technology, and then functional KCG fabrics were prepared. With one-sun simulation irradiation, the surface temperature of KCG-K3 fabric and artificial skin under cover increased by about 10℃and 8℃ compared with cotton fabric, respectively, showing active light absorption and thermal production functions. After turning off light source, KCG-K3 fabric shows obvious heat storage and insulation section, with temperature of the skin under cover about 3℃ higher than that of pure cotton fabric, which has wonderful passive thermal insulation function. The rapid moisture absorption and permeability of KCG-K3 fabric are also superior to cotton fabric. The combined effect of photothermal conversion and moisture absorption can promote sweat evaporation with outstanding evaporation rate (0.047 g·min-1). More importantly, the KCG-K3 textile has excellent UV-blocking property, with a UPF value 2.5 times that of cotton fabric. In outdoor test, it was proved that KCG-K3 textile were 5 ℃ higher than cotton textile under direct sunlight. The tensile and abrasion resistance properties of KCG-K3 fabric are superior to those of cotton fabric. Functional KCG-K3 fabric with excellent thermal and moisture management property, providing a warm and dry microenvironment for the skin of cold-weather athletes. This research offers a simple and eco-friendly approach for the development of PTAMM system. • Green graphene/kapok textiles for personal thermal and moisture management. • Radiation heating and heat buffering achieved by composite yarn design. • One-way moisture transport and fast vaporing for dry-fresh exercise environment. • High-quality kapok fiber yarns using embeddable and locatable spinning technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".